Overview

Dataset statistics

Number of variables27
Number of observations4998
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory224.0 B

Variable types

Categorical3
Text9
Numeric15

Alerts

color is highly imbalanced (75.1%)Imbalance
language is highly imbalanced (88.8%)Imbalance
budget is highly skewed (γ1 = 50.46992572)Skewed
director_facebook_likes has 897 (17.9%) zerosZeros
actor_3_facebook_likes has 89 (1.8%) zerosZeros
actor_2_facebook_likes has 55 (1.1%) zerosZeros
movie_facebook_likes has 2162 (43.3%) zerosZeros

Reproduction

Analysis started2024-03-17 00:42:15.818007
Analysis finished2024-03-17 00:43:02.917350
Duration47.1 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

color
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Color
4791 
Black and White
 
207

Length

Max length16
Median length5
Mean length5.4555822
Min length5

Characters and Unicode

Total characters27267
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowColor
2nd rowColor
3rd rowColor
4th rowColor
5th rowColor

Common Values

ValueCountFrequency (%)
Color 4791
95.9%
Black and White 207
 
4.1%

Length

2024-03-17T01:43:03.043284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T01:43:03.214473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
color 4791
88.5%
black 207
 
3.8%
and 207
 
3.8%
white 207
 
3.8%

Most occurring characters

ValueCountFrequency (%)
o 9582
35.1%
l 4998
18.3%
C 4791
17.6%
r 4791
17.6%
621
 
2.3%
a 414
 
1.5%
B 207
 
0.8%
c 207
 
0.8%
k 207
 
0.8%
n 207
 
0.8%
Other values (6) 1242
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21441
78.6%
Uppercase Letter 5205
 
19.1%
Space Separator 621
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 9582
44.7%
l 4998
23.3%
r 4791
22.3%
a 414
 
1.9%
c 207
 
1.0%
k 207
 
1.0%
n 207
 
1.0%
d 207
 
1.0%
h 207
 
1.0%
i 207
 
1.0%
Other values (2) 414
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
C 4791
92.0%
B 207
 
4.0%
W 207
 
4.0%
Space Separator
ValueCountFrequency (%)
621
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26646
97.7%
Common 621
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 9582
36.0%
l 4998
18.8%
C 4791
18.0%
r 4791
18.0%
a 414
 
1.6%
B 207
 
0.8%
c 207
 
0.8%
k 207
 
0.8%
n 207
 
0.8%
d 207
 
0.8%
Other values (5) 1035
 
3.9%
Common
ValueCountFrequency (%)
621
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 9582
35.1%
l 4998
18.3%
C 4791
17.6%
r 4791
17.6%
621
 
2.3%
a 414
 
1.5%
B 207
 
0.8%
c 207
 
0.8%
k 207
 
0.8%
n 207
 
0.8%
Other values (6) 1242
 
4.6%
Distinct2399
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:03.683549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length32
Median length24
Mean length12.966186
Min length3

Characters and Unicode

Total characters64805
Distinct characters76
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1513 ?
Unique (%)30.3%

Sample

1st rowJames Cameron
2nd rowGore Verbinski
3rd rowSam Mendes
4th rowChristopher Nolan
5th rowDoug Walker
ValueCountFrequency (%)
john 178
 
1.7%
david 148
 
1.4%
michael 126
 
1.2%
unknown 103
 
1.0%
james 87
 
0.8%
robert 84
 
0.8%
peter 84
 
0.8%
richard 80
 
0.8%
paul 76
 
0.7%
scott 65
 
0.6%
Other values (2967) 9254
90.0%
2024-03-17T01:43:04.464715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6045
 
9.3%
5287
 
8.2%
a 5237
 
8.1%
n 4928
 
7.6%
r 4415
 
6.8%
o 3861
 
6.0%
i 3665
 
5.7%
l 2947
 
4.5%
t 2297
 
3.5%
s 2070
 
3.2%
Other values (66) 24053
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48774
75.3%
Uppercase Letter 10398
 
16.0%
Space Separator 5287
 
8.2%
Other Punctuation 259
 
0.4%
Dash Punctuation 87
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6045
12.4%
a 5237
10.7%
n 4928
10.1%
r 4415
 
9.1%
o 3861
 
7.9%
i 3665
 
7.5%
l 2947
 
6.0%
t 2297
 
4.7%
s 2070
 
4.2%
h 1833
 
3.8%
Other values (31) 11476
23.5%
Uppercase Letter
ValueCountFrequency (%)
S 995
 
9.6%
J 916
 
8.8%
M 881
 
8.5%
R 752
 
7.2%
C 704
 
6.8%
B 669
 
6.4%
D 614
 
5.9%
A 564
 
5.4%
L 496
 
4.8%
P 481
 
4.6%
Other values (21) 3326
32.0%
Other Punctuation
ValueCountFrequency (%)
. 238
91.9%
' 21
 
8.1%
Space Separator
ValueCountFrequency (%)
5287
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 87
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59172
91.3%
Common 5633
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6045
 
10.2%
a 5237
 
8.9%
n 4928
 
8.3%
r 4415
 
7.5%
o 3861
 
6.5%
i 3665
 
6.2%
l 2947
 
5.0%
t 2297
 
3.9%
s 2070
 
3.5%
h 1833
 
3.1%
Other values (62) 21874
37.0%
Common
ValueCountFrequency (%)
5287
93.9%
. 238
 
4.2%
- 87
 
1.5%
' 21
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64663
99.8%
None 142
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6045
 
9.3%
5287
 
8.2%
a 5237
 
8.1%
n 4928
 
7.6%
r 4415
 
6.8%
o 3861
 
6.0%
i 3665
 
5.7%
l 2947
 
4.6%
t 2297
 
3.6%
s 2070
 
3.2%
Other values (46) 23911
37.0%
None
ValueCountFrequency (%)
é 45
31.7%
á 19
13.4%
ö 16
 
11.3%
ó 16
 
11.3%
í 8
 
5.6%
ñ 7
 
4.9%
å 6
 
4.2%
ç 5
 
3.5%
É 3
 
2.1%
Ô 2
 
1.4%
Other values (10) 15
 
10.6%

num_critic_for_reviews
Real number (ℝ)

Distinct528
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.59704
Minimum1
Maximum813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:04.743637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q150
median110
Q3193
95-th percentile385.15
Maximum813
Range812
Interquartile range (IQR)143

Descriptive statistics

Standard deviation120.9164
Coefficient of variation (CV)0.86618168
Kurtosis2.9659474
Mean139.59704
Median Absolute Deviation (MAD)67
Skewness1.5297719
Sum697706
Variance14620.775
MonotonicityNot monotonic
2024-03-17T01:43:05.005635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 68
 
1.4%
1 42
 
0.8%
9 36
 
0.7%
5 36
 
0.7%
10 35
 
0.7%
8 34
 
0.7%
12 34
 
0.7%
16 33
 
0.7%
81 32
 
0.6%
43 31
 
0.6%
Other values (518) 4617
92.4%
ValueCountFrequency (%)
1 42
0.8%
2 26
0.5%
3 24
0.5%
4 29
0.6%
5 36
0.7%
6 28
0.6%
7 23
0.5%
8 34
0.7%
9 36
0.7%
10 35
0.7%
ValueCountFrequency (%)
813 1
< 0.1%
775 1
< 0.1%
765 1
< 0.1%
750 2
< 0.1%
739 1
< 0.1%
738 1
< 0.1%
733 1
< 0.1%
723 1
< 0.1%
712 1
< 0.1%
703 1
< 0.1%

duration
Real number (ℝ)

Distinct191
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.20068
Minimum7
Maximum511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:05.257309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile81
Q193
median103
Q3118
95-th percentile146
Maximum511
Range504
Interquartile range (IQR)25

Descriptive statistics

Standard deviation25.211904
Coefficient of variation (CV)0.23518418
Kurtosis22.645561
Mean107.20068
Median Absolute Deviation (MAD)12
Skewness2.346182
Sum535789
Variance635.64013
MonotonicityNot monotonic
2024-03-17T01:43:05.513845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 160
 
3.2%
100 139
 
2.8%
101 136
 
2.7%
98 135
 
2.7%
97 131
 
2.6%
93 127
 
2.5%
94 124
 
2.5%
99 123
 
2.5%
95 122
 
2.4%
103 116
 
2.3%
Other values (181) 3685
73.7%
ValueCountFrequency (%)
7 2
 
< 0.1%
11 1
 
< 0.1%
14 1
 
< 0.1%
20 1
 
< 0.1%
22 7
0.1%
23 2
 
< 0.1%
24 2
 
< 0.1%
25 4
0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
511 1
< 0.1%
334 1
< 0.1%
330 1
< 0.1%
325 1
< 0.1%
300 1
< 0.1%
293 1
< 0.1%
289 1
< 0.1%
286 1
< 0.1%
280 1
< 0.1%
271 1
< 0.1%

director_facebook_likes
Real number (ℝ)

ZEROS 

Distinct435
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean675.4964
Minimum0
Maximum23000
Zeros897
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:05.748992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median49
Q3189
95-th percentile964
Maximum23000
Range23000
Interquartile range (IQR)182

Descriptive statistics

Standard deviation2793.8965
Coefficient of variation (CV)4.1360642
Kurtosis27.785224
Mean675.4964
Median Absolute Deviation (MAD)49
Skewness5.2780979
Sum3376131
Variance7805857.5
MonotonicityNot monotonic
2024-03-17T01:43:05.976981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 897
 
17.9%
49 124
 
2.5%
3 70
 
1.4%
6 66
 
1.3%
7 64
 
1.3%
2 63
 
1.3%
4 60
 
1.2%
11 58
 
1.2%
10 53
 
1.1%
5 52
 
1.0%
Other values (425) 3491
69.8%
ValueCountFrequency (%)
0 897
17.9%
2 63
 
1.3%
3 70
 
1.4%
4 60
 
1.2%
5 52
 
1.0%
6 66
 
1.3%
7 64
 
1.3%
8 52
 
1.0%
9 49
 
1.0%
10 53
 
1.1%
ValueCountFrequency (%)
23000 1
 
< 0.1%
22000 8
 
0.2%
21000 10
 
0.2%
20000 1
 
< 0.1%
18000 4
 
0.1%
17000 20
0.4%
16000 28
0.6%
15000 2
 
< 0.1%
14000 30
0.6%
13000 26
0.5%

actor_3_facebook_likes
Real number (ℝ)

ZEROS 

Distinct906
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean638.65426
Minimum0
Maximum23000
Zeros89
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:06.215807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q1134
median369
Q3634.75
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)500.75

Descriptive statistics

Standard deviation1639.6146
Coefficient of variation (CV)2.5672961
Kurtosis61.39995
Mean638.65426
Median Absolute Deviation (MAD)247
Skewness7.3191584
Sum3191994
Variance2688336
MonotonicityNot monotonic
2024-03-17T01:43:06.455208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 125
 
2.5%
0 89
 
1.8%
11000 29
 
0.6%
3 27
 
0.5%
2000 27
 
0.5%
3000 26
 
0.5%
369 24
 
0.5%
7 21
 
0.4%
4 21
 
0.4%
826 21
 
0.4%
Other values (896) 4588
91.8%
ValueCountFrequency (%)
0 89
1.8%
2 21
 
0.4%
3 27
 
0.5%
4 21
 
0.4%
5 18
 
0.4%
6 18
 
0.4%
7 21
 
0.4%
8 17
 
0.3%
9 16
 
0.3%
10 12
 
0.2%
ValueCountFrequency (%)
23000 2
 
< 0.1%
20000 1
 
< 0.1%
19000 4
 
0.1%
17000 1
 
< 0.1%
16000 3
 
0.1%
15000 1
 
< 0.1%
14000 6
 
0.1%
13000 5
 
0.1%
12000 7
 
0.1%
11000 29
0.6%
Distinct3033
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:06.891082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length28
Median length25
Mean length13.057423
Min length3

Characters and Unicode

Total characters65261
Distinct characters80
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2104 ?
Unique (%)42.1%

Sample

1st rowJoel David Moore
2nd rowOrlando Bloom
3rd rowRory Kinnear
4th rowChristian Bale
5th rowRob Walker
ValueCountFrequency (%)
michael 102
 
1.0%
david 59
 
0.6%
john 56
 
0.5%
james 53
 
0.5%
scott 51
 
0.5%
tom 50
 
0.5%
robert 43
 
0.4%
jason 43
 
0.4%
kevin 41
 
0.4%
thomas 39
 
0.4%
Other values (3826) 9784
94.8%
2024-03-17T01:43:07.590851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6152
 
9.4%
a 5882
 
9.0%
5323
 
8.2%
n 4748
 
7.3%
r 4365
 
6.7%
i 3987
 
6.1%
o 3632
 
5.6%
l 3389
 
5.2%
t 2326
 
3.6%
s 2139
 
3.3%
Other values (70) 23318
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49092
75.2%
Uppercase Letter 10591
 
16.2%
Space Separator 5323
 
8.2%
Other Punctuation 185
 
0.3%
Dash Punctuation 64
 
0.1%
Decimal Number 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6152
12.5%
a 5882
12.0%
n 4748
9.7%
r 4365
8.9%
i 3987
 
8.1%
o 3632
 
7.4%
l 3389
 
6.9%
t 2326
 
4.7%
s 2139
 
4.4%
h 1785
 
3.6%
Other values (38) 10687
21.8%
Uppercase Letter
ValueCountFrequency (%)
M 990
 
9.3%
S 812
 
7.7%
C 805
 
7.6%
B 766
 
7.2%
J 765
 
7.2%
D 657
 
6.2%
A 637
 
6.0%
R 586
 
5.5%
L 506
 
4.8%
T 458
 
4.3%
Other values (16) 3609
34.1%
Other Punctuation
ValueCountFrequency (%)
. 122
65.9%
' 63
34.1%
Decimal Number
ValueCountFrequency (%)
5 3
50.0%
0 3
50.0%
Space Separator
ValueCountFrequency (%)
5323
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59683
91.5%
Common 5578
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6152
 
10.3%
a 5882
 
9.9%
n 4748
 
8.0%
r 4365
 
7.3%
i 3987
 
6.7%
o 3632
 
6.1%
l 3389
 
5.7%
t 2326
 
3.9%
s 2139
 
3.6%
h 1785
 
3.0%
Other values (64) 21278
35.7%
Common
ValueCountFrequency (%)
5323
95.4%
. 122
 
2.2%
- 64
 
1.1%
' 63
 
1.1%
5 3
 
0.1%
0 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65140
99.8%
None 121
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6152
 
9.4%
a 5882
 
9.0%
5323
 
8.2%
n 4748
 
7.3%
r 4365
 
6.7%
i 3987
 
6.1%
o 3632
 
5.6%
l 3389
 
5.2%
t 2326
 
3.6%
s 2139
 
3.3%
Other values (48) 23197
35.6%
None
ValueCountFrequency (%)
é 43
35.5%
í 14
 
11.6%
á 10
 
8.3%
ë 8
 
6.6%
ø 6
 
5.0%
ó 6
 
5.0%
ü 4
 
3.3%
å 4
 
3.3%
û 3
 
2.5%
ï 3
 
2.5%
Other values (12) 20
16.5%

actor_1_facebook_likes
Real number (ℝ)

Distinct878
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6549.1347
Minimum0
Maximum640000
Zeros26
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:08.059398image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile94
Q1613
median984
Q311000
95-th percentile24000
Maximum640000
Range640000
Interquartile range (IQR)10387

Descriptive statistics

Standard deviation15052.477
Coefficient of variation (CV)2.2983917
Kurtosis683.01081
Mean6549.1347
Median Absolute Deviation (MAD)744.5
Skewness19.144076
Sum32732575
Variance2.2657706 × 108
MonotonicityNot monotonic
2024-03-17T01:43:08.313815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 443
 
8.9%
11000 209
 
4.2%
2000 193
 
3.9%
3000 152
 
3.0%
12000 133
 
2.7%
13000 127
 
2.5%
14000 122
 
2.4%
10000 112
 
2.2%
18000 108
 
2.2%
22000 81
 
1.6%
Other values (868) 3318
66.4%
ValueCountFrequency (%)
0 26
0.5%
2 8
 
0.2%
3 4
 
0.1%
4 2
 
< 0.1%
5 7
 
0.1%
6 3
 
0.1%
7 3
 
0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
640000 1
 
< 0.1%
260000 3
 
0.1%
164000 2
 
< 0.1%
137000 2
 
< 0.1%
87000 8
 
0.2%
77000 1
 
< 0.1%
49000 27
0.5%
46000 1
 
< 0.1%
45000 5
 
0.1%
44000 2
 
< 0.1%

gross
Real number (ℝ)

Distinct4036
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44324642
Minimum162
Maximum7.6050585 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:08.560750image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile126085.3
Q18382841.2
median25445749
Q351376923
95-th percentile1.6611752 × 108
Maximum7.6050585 × 108
Range7.6050568 × 108
Interquartile range (IQR)42994082

Descriptive statistics

Standard deviation62344554
Coefficient of variation (CV)1.4065439
Kurtosis18.006554
Mean44324642
Median Absolute Deviation (MAD)19334093
Skewness3.4652646
Sum2.2153456 × 1011
Variance3.8868434 × 1015
MonotonicityNot monotonic
2024-03-17T01:43:08.824338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25445749 874
 
17.5%
177343675 3
 
0.1%
218051260 3
 
0.1%
8000000 3
 
0.1%
3000000 3
 
0.1%
25000000 2
 
< 0.1%
10654581 2
 
< 0.1%
21378000 2
 
< 0.1%
22494487 2
 
< 0.1%
26505000 2
 
< 0.1%
Other values (4026) 4102
82.1%
ValueCountFrequency (%)
162 1
< 0.1%
703 1
< 0.1%
721 1
< 0.1%
728 1
< 0.1%
828 1
< 0.1%
1111 1
< 0.1%
1332 1
< 0.1%
1521 1
< 0.1%
1711 1
< 0.1%
2245 1
< 0.1%
ValueCountFrequency (%)
760505847 1
< 0.1%
658672302 1
< 0.1%
652177271 1
< 0.1%
623279547 1
< 0.1%
533316061 1
< 0.1%
474544677 1
< 0.1%
460935665 1
< 0.1%
458991599 1
< 0.1%
448130642 1
< 0.1%
436471036 1
< 0.1%

genres
Text

Distinct914
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:09.126234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length64
Median length53
Mean length20.32433
Min length5

Characters and Unicode

Total characters101581
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique499 ?
Unique (%)10.0%

Sample

1st rowAction|Adventure|Fantasy|Sci-Fi
2nd rowAction|Adventure|Fantasy
3rd rowAction|Adventure|Thriller
4th rowAction|Thriller
5th rowDocumentary
ValueCountFrequency (%)
drama 235
 
4.7%
comedy 205
 
4.1%
comedy|drama 189
 
3.8%
comedy|drama|romance 187
 
3.7%
comedy|romance 158
 
3.2%
drama|romance 151
 
3.0%
crime|drama|thriller 100
 
2.0%
horror 70
 
1.4%
action|crime|drama|thriller 67
 
1.3%
drama|thriller 64
 
1.3%
Other values (904) 3572
71.5%
2024-03-17T01:43:09.716792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 10436
 
10.3%
| 9384
 
9.2%
a 8993
 
8.9%
e 7875
 
7.8%
m 7328
 
7.2%
i 6527
 
6.4%
o 6268
 
6.2%
y 4616
 
4.5%
n 4458
 
4.4%
t 4004
 
3.9%
Other values (25) 31692
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76573
75.4%
Uppercase Letter 15004
 
14.8%
Math Symbol 9384
 
9.2%
Dash Punctuation 620
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 10436
13.6%
a 8993
11.7%
e 7875
10.3%
m 7328
9.6%
i 6527
8.5%
o 6268
8.2%
y 4616
 
6.0%
n 4458
 
5.8%
t 4004
 
5.2%
l 3476
 
4.5%
Other values (9) 12592
16.4%
Uppercase Letter
ValueCountFrequency (%)
C 2745
18.3%
D 2692
17.9%
A 2299
15.3%
F 1765
11.8%
T 1398
9.3%
R 1100
7.3%
M 837
 
5.6%
S 798
 
5.3%
H 761
 
5.1%
W 305
 
2.0%
Other values (4) 304
 
2.0%
Math Symbol
ValueCountFrequency (%)
| 9384
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91577
90.2%
Common 10004
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 10436
 
11.4%
a 8993
 
9.8%
e 7875
 
8.6%
m 7328
 
8.0%
i 6527
 
7.1%
o 6268
 
6.8%
y 4616
 
5.0%
n 4458
 
4.9%
t 4004
 
4.4%
l 3476
 
3.8%
Other values (23) 27596
30.1%
Common
ValueCountFrequency (%)
| 9384
93.8%
- 620
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101581
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 10436
 
10.3%
| 9384
 
9.2%
a 8993
 
8.9%
e 7875
 
7.8%
m 7328
 
7.2%
i 6527
 
6.4%
o 6268
 
6.2%
y 4616
 
4.5%
n 4458
 
4.4%
t 4004
 
3.9%
Other values (25) 31692
31.2%
Distinct2098
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:10.169707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length27
Median length24
Mean length13.185274
Min length4

Characters and Unicode

Total characters65900
Distinct characters76
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1369 ?
Unique (%)27.4%

Sample

1st rowCCH Pounder
2nd rowJohnny Depp
3rd rowChristoph Waltz
4th rowTom Hardy
5th rowDoug Walker
ValueCountFrequency (%)
robert 108
 
1.0%
tom 92
 
0.9%
michael 89
 
0.9%
de 57
 
0.6%
jason 57
 
0.6%
james 54
 
0.5%
bruce 51
 
0.5%
steve 50
 
0.5%
niro 49
 
0.5%
jr 48
 
0.5%
Other values (2889) 9702
93.7%
2024-03-17T01:43:10.860011image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6157
 
9.3%
a 5692
 
8.6%
5359
 
8.1%
n 4797
 
7.3%
r 4278
 
6.5%
i 4204
 
6.4%
o 3891
 
5.9%
l 3287
 
5.0%
t 2546
 
3.9%
s 2326
 
3.5%
Other values (66) 23363
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49627
75.3%
Uppercase Letter 10615
 
16.1%
Space Separator 5359
 
8.1%
Other Punctuation 225
 
0.3%
Dash Punctuation 72
 
0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6157
12.4%
a 5692
11.5%
n 4797
9.7%
r 4278
8.6%
i 4204
 
8.5%
o 3891
 
7.8%
l 3287
 
6.6%
t 2546
 
5.1%
s 2326
 
4.7%
h 1770
 
3.6%
Other values (32) 10679
21.5%
Uppercase Letter
ValueCountFrequency (%)
J 940
 
8.9%
M 904
 
8.5%
S 845
 
8.0%
C 808
 
7.6%
B 738
 
7.0%
D 718
 
6.8%
R 628
 
5.9%
H 519
 
4.9%
A 499
 
4.7%
L 487
 
4.6%
Other values (18) 3529
33.2%
Other Punctuation
ValueCountFrequency (%)
. 178
79.1%
' 47
 
20.9%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
0 1
50.0%
Space Separator
ValueCountFrequency (%)
5359
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60242
91.4%
Common 5658
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6157
 
10.2%
a 5692
 
9.4%
n 4797
 
8.0%
r 4278
 
7.1%
i 4204
 
7.0%
o 3891
 
6.5%
l 3287
 
5.5%
t 2546
 
4.2%
s 2326
 
3.9%
h 1770
 
2.9%
Other values (60) 21294
35.3%
Common
ValueCountFrequency (%)
5359
94.7%
. 178
 
3.1%
- 72
 
1.3%
' 47
 
0.8%
5 1
 
< 0.1%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65821
99.9%
None 79
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6157
 
9.4%
a 5692
 
8.6%
5359
 
8.1%
n 4797
 
7.3%
r 4278
 
6.5%
i 4204
 
6.4%
o 3891
 
5.9%
l 3287
 
5.0%
t 2546
 
3.9%
s 2326
 
3.5%
Other values (48) 23284
35.4%
None
ValueCountFrequency (%)
é 19
24.1%
ë 15
19.0%
á 7
 
8.9%
í 6
 
7.6%
å 5
 
6.3%
ç 5
 
6.3%
ø 4
 
5.1%
Ó 3
 
3.8%
ü 2
 
2.5%
Á 2
 
2.5%
Other values (8) 11
13.9%
Distinct4916
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:11.355079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length86
Median length58
Mean length15.309324
Min length1

Characters and Unicode

Total characters76516
Distinct characters96
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4837 ?
Unique (%)96.8%

Sample

1st rowAvatar
2nd rowPirates of the Caribbean: At World's End
3rd rowSpectre
4th rowThe Dark Knight Rises
5th rowStar Wars: Episode VII - The Force Awakens
ValueCountFrequency (%)
the 1591
 
11.5%
of 480
 
3.5%
a 188
 
1.4%
and 148
 
1.1%
in 123
 
0.9%
to 106
 
0.8%
2 103
 
0.7%
80
 
0.6%
man 66
 
0.5%
love 55
 
0.4%
Other values (4905) 10906
78.8%
2024-03-17T01:43:12.137873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8848
 
11.6%
e 7839
 
10.2%
a 4807
 
6.3%
o 4628
 
6.0%
n 4104
 
5.4%
r 4103
 
5.4%
i 3907
 
5.1%
t 3788
 
5.0%
s 2981
 
3.9%
h 2952
 
3.9%
Other values (86) 28559
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53945
70.5%
Uppercase Letter 12136
 
15.9%
Space Separator 8848
 
11.6%
Other Punctuation 948
 
1.2%
Decimal Number 525
 
0.7%
Dash Punctuation 94
 
0.1%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%
Currency Symbol 4
 
< 0.1%
Other Number 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7839
14.5%
a 4807
 
8.9%
o 4628
 
8.6%
n 4104
 
7.6%
r 4103
 
7.6%
i 3907
 
7.2%
t 3788
 
7.0%
s 2981
 
5.5%
h 2952
 
5.5%
l 2509
 
4.7%
Other values (25) 12327
22.9%
Uppercase Letter
ValueCountFrequency (%)
T 1707
14.1%
S 1048
 
8.6%
M 818
 
6.7%
B 773
 
6.4%
D 722
 
5.9%
C 681
 
5.6%
A 660
 
5.4%
L 573
 
4.7%
H 561
 
4.6%
W 502
 
4.1%
Other values (17) 4091
33.7%
Other Punctuation
ValueCountFrequency (%)
: 369
38.9%
' 230
24.3%
. 145
 
15.3%
, 78
 
8.2%
& 61
 
6.4%
! 32
 
3.4%
? 16
 
1.7%
/ 8
 
0.8%
* 5
 
0.5%
# 2
 
0.2%
Other values (2) 2
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 146
27.8%
3 87
16.6%
0 86
16.4%
1 82
15.6%
4 35
 
6.7%
8 22
 
4.2%
5 21
 
4.0%
9 17
 
3.2%
7 15
 
2.9%
6 14
 
2.7%
Open Punctuation
ValueCountFrequency (%)
( 3
60.0%
[ 2
40.0%
Close Punctuation
ValueCountFrequency (%)
) 3
60.0%
] 2
40.0%
Currency Symbol
ValueCountFrequency (%)
¢ 2
50.0%
$ 2
50.0%
Space Separator
ValueCountFrequency (%)
8848
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 94
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66081
86.4%
Common 10435
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7839
 
11.9%
a 4807
 
7.3%
o 4628
 
7.0%
n 4104
 
6.2%
r 4103
 
6.2%
i 3907
 
5.9%
t 3788
 
5.7%
s 2981
 
4.5%
h 2952
 
4.5%
l 2509
 
3.8%
Other values (52) 24463
37.0%
Common
ValueCountFrequency (%)
8848
84.8%
: 369
 
3.5%
' 230
 
2.2%
2 146
 
1.4%
. 145
 
1.4%
- 94
 
0.9%
3 87
 
0.8%
0 86
 
0.8%
1 82
 
0.8%
, 78
 
0.7%
Other values (24) 270
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76493
> 99.9%
None 23
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8848
 
11.6%
e 7839
 
10.2%
a 4807
 
6.3%
o 4628
 
6.1%
n 4104
 
5.4%
r 4103
 
5.4%
i 3907
 
5.1%
t 3788
 
5.0%
s 2981
 
3.9%
h 2952
 
3.9%
Other values (72) 28536
37.3%
None
ValueCountFrequency (%)
é 8
34.8%
½ 2
 
8.7%
¢ 2
 
8.7%
ü 1
 
4.3%
è 1
 
4.3%
· 1
 
4.3%
à 1
 
4.3%
Æ 1
 
4.3%
ä 1
 
4.3%
á 1
 
4.3%
Other values (4) 4
17.4%

num_voted_users
Real number (ℝ)

Distinct4826
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83470.199
Minimum5
Maximum1689764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:12.428284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile511.55
Q18560
median34260.5
Q396120.75
95-th percentile332096.65
Maximum1689764
Range1689759
Interquartile range (IQR)87560.75

Descriptive statistics

Standard deviation138086.56
Coefficient of variation (CV)1.6543217
Kurtosis24.611289
Mean83470.199
Median Absolute Deviation (MAD)30735
Skewness4.0349324
Sum4.1718405 × 108
Variance1.9067898 × 1010
MonotonicityNot monotonic
2024-03-17T01:43:12.690577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 5
 
0.1%
6 4
 
0.1%
2541 3
 
0.1%
38 3
 
0.1%
162 3
 
0.1%
62 3
 
0.1%
3665 3
 
0.1%
8 3
 
0.1%
53 3
 
0.1%
3119 3
 
0.1%
Other values (4816) 4965
99.3%
ValueCountFrequency (%)
5 2
< 0.1%
6 4
0.1%
7 2
< 0.1%
8 3
0.1%
10 1
 
< 0.1%
13 1
 
< 0.1%
15 2
< 0.1%
16 1
 
< 0.1%
18 2
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
1689764 1
< 0.1%
1676169 1
< 0.1%
1468200 1
< 0.1%
1347461 1
< 0.1%
1324680 1
< 0.1%
1251222 1
< 0.1%
1238746 1
< 0.1%
1217752 1
< 0.1%
1215718 1
< 0.1%
1155770 1
< 0.1%

cast_total_facebook_likes
Real number (ℝ)

Distinct3978
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9676.9412
Minimum0
Maximum656730
Zeros33
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:12.947395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile175.85
Q11405.5
median3085.5
Q313740.5
95-th percentile36892.75
Maximum656730
Range656730
Interquartile range (IQR)12335

Descriptive statistics

Standard deviation18165.405
Coefficient of variation (CV)1.8771846
Kurtosis364.38585
Mean9676.9412
Median Absolute Deviation (MAD)2302.5
Skewness12.923944
Sum48365352
Variance3.2998192 × 108
MonotonicityNot monotonic
2024-03-17T01:43:13.201590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
0.7%
5 7
 
0.1%
2 6
 
0.1%
2020 6
 
0.1%
29 5
 
0.1%
1044 5
 
0.1%
673 5
 
0.1%
1233 4
 
0.1%
2251 4
 
0.1%
2990 4
 
0.1%
Other values (3968) 4919
98.4%
ValueCountFrequency (%)
0 33
0.7%
2 6
 
0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 7
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
656730 1
< 0.1%
303717 1
< 0.1%
283939 1
< 0.1%
263584 1
< 0.1%
261818 1
< 0.1%
170118 1
< 0.1%
140268 1
< 0.1%
137712 1
< 0.1%
120797 1
< 0.1%
108016 1
< 0.1%
Distinct3522
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:13.703105image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length29
Median length25
Mean length13.052621
Min length3

Characters and Unicode

Total characters65237
Distinct characters81
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2669 ?
Unique (%)53.4%

Sample

1st rowWes Studi
2nd rowJack Davenport
3rd rowStephanie Sigman
4th rowJoseph Gordon-Levitt
5th rowunknown
ValueCountFrequency (%)
michael 85
 
0.8%
john 78
 
0.8%
david 70
 
0.7%
james 69
 
0.7%
robert 46
 
0.4%
tom 43
 
0.4%
kevin 41
 
0.4%
paul 41
 
0.4%
peter 38
 
0.4%
steve 36
 
0.3%
Other values (4308) 9775
94.7%
2024-03-17T01:43:14.477096image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6135
 
9.4%
a 5948
 
9.1%
5324
 
8.2%
n 4619
 
7.1%
r 4144
 
6.4%
i 3939
 
6.0%
o 3565
 
5.5%
l 3475
 
5.3%
t 2336
 
3.6%
s 2312
 
3.5%
Other values (71) 23440
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49008
75.1%
Uppercase Letter 10593
 
16.2%
Space Separator 5324
 
8.2%
Other Punctuation 231
 
0.4%
Dash Punctuation 79
 
0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6135
12.5%
a 5948
12.1%
n 4619
9.4%
r 4144
 
8.5%
i 3939
 
8.0%
o 3565
 
7.3%
l 3475
 
7.1%
t 2336
 
4.8%
s 2312
 
4.7%
h 1838
 
3.8%
Other values (34) 10697
21.8%
Uppercase Letter
ValueCountFrequency (%)
M 974
 
9.2%
S 822
 
7.8%
J 822
 
7.8%
B 799
 
7.5%
C 785
 
7.4%
D 648
 
6.1%
R 612
 
5.8%
A 584
 
5.5%
L 530
 
5.0%
K 461
 
4.4%
Other values (21) 3556
33.6%
Other Punctuation
ValueCountFrequency (%)
. 168
72.7%
' 63
 
27.3%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
0 1
50.0%
Space Separator
ValueCountFrequency (%)
5324
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59601
91.4%
Common 5636
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6135
 
10.3%
a 5948
 
10.0%
n 4619
 
7.7%
r 4144
 
7.0%
i 3939
 
6.6%
o 3565
 
6.0%
l 3475
 
5.8%
t 2336
 
3.9%
s 2312
 
3.9%
h 1838
 
3.1%
Other values (65) 21290
35.7%
Common
ValueCountFrequency (%)
5324
94.5%
. 168
 
3.0%
- 79
 
1.4%
' 63
 
1.1%
5 1
 
< 0.1%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65103
99.8%
None 134
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6135
 
9.4%
a 5948
 
9.1%
5324
 
8.2%
n 4619
 
7.1%
r 4144
 
6.4%
i 3939
 
6.1%
o 3565
 
5.5%
l 3475
 
5.3%
t 2336
 
3.6%
s 2312
 
3.6%
Other values (48) 23306
35.8%
None
ValueCountFrequency (%)
é 49
36.6%
í 14
 
10.4%
á 13
 
9.7%
ó 9
 
6.7%
ü 7
 
5.2%
ë 7
 
5.2%
à 5
 
3.7%
è 4
 
3.0%
ç 3
 
2.2%
ô 3
 
2.2%
Other values (13) 20
14.9%
Distinct4761
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:14.903598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length149
Median length102
Mean length50.960984
Min length2

Characters and Unicode

Total characters254703
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4679 ?
Unique (%)93.6%

Sample

1st rowavatar|future|marine|native|paraplegic
2nd rowgoddess|marriage ceremony|marriage proposal|pirate|singapore
3rd rowbomb|espionage|sequel|spy|terrorist
4th rowdeception|imprisonment|lawlessness|police officer|terrorist plot
5th rownone
ValueCountFrequency (%)
in 331
 
1.8%
of 219
 
1.2%
on 209
 
1.2%
the 189
 
1.0%
a 183
 
1.0%
to 176
 
1.0%
none 152
 
0.8%
york 122
 
0.7%
based 106
 
0.6%
female 104
 
0.6%
Other values (11487) 16224
90.1%
2024-03-17T01:43:15.628886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 24738
 
9.7%
a 19403
 
7.6%
| 19035
 
7.5%
i 18589
 
7.3%
r 17953
 
7.0%
t 16049
 
6.3%
n 15836
 
6.2%
o 15492
 
6.1%
s 13176
 
5.2%
13017
 
5.1%
Other values (32) 81415
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 221304
86.9%
Math Symbol 19035
 
7.5%
Space Separator 13017
 
5.1%
Decimal Number 1127
 
0.4%
Other Punctuation 218
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 24738
11.2%
a 19403
 
8.8%
i 18589
 
8.4%
r 17953
 
8.1%
t 16049
 
7.3%
n 15836
 
7.2%
o 15492
 
7.0%
s 13176
 
6.0%
l 11079
 
5.0%
c 9377
 
4.2%
Other values (16) 59612
26.9%
Decimal Number
ValueCountFrequency (%)
1 283
25.1%
0 269
23.9%
9 221
19.6%
2 81
 
7.2%
8 65
 
5.8%
7 49
 
4.3%
5 47
 
4.2%
3 44
 
3.9%
6 38
 
3.4%
4 30
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 130
59.6%
' 88
40.4%
Math Symbol
ValueCountFrequency (%)
| 19035
100.0%
Space Separator
ValueCountFrequency (%)
13017
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 221304
86.9%
Common 33399
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 24738
11.2%
a 19403
 
8.8%
i 18589
 
8.4%
r 17953
 
8.1%
t 16049
 
7.3%
n 15836
 
7.2%
o 15492
 
7.0%
s 13176
 
6.0%
l 11079
 
5.0%
c 9377
 
4.2%
Other values (16) 59612
26.9%
Common
ValueCountFrequency (%)
| 19035
57.0%
13017
39.0%
1 283
 
0.8%
0 269
 
0.8%
9 221
 
0.7%
. 130
 
0.4%
' 88
 
0.3%
2 81
 
0.2%
8 65
 
0.2%
7 49
 
0.1%
Other values (6) 161
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 254703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 24738
 
9.7%
a 19403
 
7.6%
| 19035
 
7.5%
i 18589
 
7.3%
r 17953
 
7.0%
t 16049
 
6.3%
n 15836
 
6.2%
o 15492
 
6.1%
s 13176
 
5.2%
13017
 
5.1%
Other values (32) 81415
32.0%
Distinct4919
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:16.032396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length52
Median length52
Mean length52
Min length52

Characters and Unicode

Total characters259896
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4843 ?
Unique (%)96.9%

Sample

1st rowhttp://www.imdb.com/title/tt0499549/?ref_=fn_tt_tt_1
2nd rowhttp://www.imdb.com/title/tt0449088/?ref_=fn_tt_tt_1
3rd rowhttp://www.imdb.com/title/tt2379713/?ref_=fn_tt_tt_1
4th rowhttp://www.imdb.com/title/tt1345836/?ref_=fn_tt_tt_1
5th rowhttp://www.imdb.com/title/tt5289954/?ref_=fn_tt_tt_1
ValueCountFrequency (%)
http://www.imdb.com/title/tt0360717/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt2638144/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt2224026/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt0075005/?ref_=fn_tt_tt_1 2
 
< 0.1%
http://www.imdb.com/title/tt0413300/?ref_=fn_tt_tt_1 2
 
< 0.1%
http://www.imdb.com/title/tt0364725/?ref_=fn_tt_tt_1 2
 
< 0.1%
http://www.imdb.com/title/tt0929632/?ref_=fn_tt_tt_1 2
 
< 0.1%
http://www.imdb.com/title/tt4178092/?ref_=fn_tt_tt_1 2
 
< 0.1%
http://www.imdb.com/title/tt0397065/?ref_=fn_tt_tt_1 2
 
< 0.1%
http://www.imdb.com/title/tt0072271/?ref_=fn_tt_tt_1 2
 
< 0.1%
Other values (4909) 4975
99.5%
2024-03-17T01:43:16.639482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 49980
19.2%
/ 24990
 
9.6%
_ 19992
 
7.7%
w 14994
 
5.8%
. 9996
 
3.8%
m 9996
 
3.8%
e 9996
 
3.8%
f 9996
 
3.8%
i 9996
 
3.8%
1 9814
 
3.8%
Other values (21) 90146
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 149940
57.7%
Other Punctuation 44982
 
17.3%
Decimal Number 39984
 
15.4%
Connector Punctuation 19992
 
7.7%
Math Symbol 4998
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 49980
33.3%
w 14994
 
10.0%
m 9996
 
6.7%
e 9996
 
6.7%
f 9996
 
6.7%
i 9996
 
6.7%
p 4998
 
3.3%
h 4998
 
3.3%
d 4998
 
3.3%
l 4998
 
3.3%
Other values (5) 24990
16.7%
Decimal Number
ValueCountFrequency (%)
1 9814
24.5%
0 6755
16.9%
2 3630
 
9.1%
3 3220
 
8.1%
4 3150
 
7.9%
8 2887
 
7.2%
9 2702
 
6.8%
6 2696
 
6.7%
7 2672
 
6.7%
5 2458
 
6.1%
Other Punctuation
ValueCountFrequency (%)
/ 24990
55.6%
. 9996
 
22.2%
: 4998
 
11.1%
? 4998
 
11.1%
Connector Punctuation
ValueCountFrequency (%)
_ 19992
100.0%
Math Symbol
ValueCountFrequency (%)
= 4998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 149940
57.7%
Common 109956
42.3%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 24990
22.7%
_ 19992
18.2%
. 9996
 
9.1%
1 9814
 
8.9%
0 6755
 
6.1%
: 4998
 
4.5%
? 4998
 
4.5%
= 4998
 
4.5%
2 3630
 
3.3%
3 3220
 
2.9%
Other values (6) 16565
15.1%
Latin
ValueCountFrequency (%)
t 49980
33.3%
w 14994
 
10.0%
m 9996
 
6.7%
e 9996
 
6.7%
f 9996
 
6.7%
i 9996
 
6.7%
p 4998
 
3.3%
h 4998
 
3.3%
d 4998
 
3.3%
l 4998
 
3.3%
Other values (5) 24990
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 259896
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 49980
19.2%
/ 24990
 
9.6%
_ 19992
 
7.7%
w 14994
 
5.8%
. 9996
 
3.8%
m 9996
 
3.8%
e 9996
 
3.8%
f 9996
 
3.8%
i 9996
 
3.8%
1 9814
 
3.8%
Other values (21) 90146
34.7%

num_user_for_reviews
Real number (ℝ)

Distinct954
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.52721
Minimum1
Maximum5060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:16.908258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q165
median156
Q3323
95-th percentile902.15
Maximum5060
Range5059
Interquartile range (IQR)258

Descriptive statistics

Standard deviation377.05627
Coefficient of variation (CV)1.38865
Kurtosis26.837621
Mean271.52721
Median Absolute Deviation (MAD)112
Skewness4.1555332
Sum1357093
Variance142171.43
MonotonicityNot monotonic
2024-03-17T01:43:17.170173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 51
 
1.0%
156 36
 
0.7%
3 33
 
0.7%
26 32
 
0.6%
2 32
 
0.6%
10 29
 
0.6%
6 28
 
0.6%
50 26
 
0.5%
8 25
 
0.5%
32 25
 
0.5%
Other values (944) 4681
93.7%
ValueCountFrequency (%)
1 51
1.0%
2 32
0.6%
3 33
0.7%
4 23
0.5%
5 19
 
0.4%
6 28
0.6%
7 17
 
0.3%
8 25
0.5%
9 23
0.5%
10 29
0.6%
ValueCountFrequency (%)
5060 1
< 0.1%
4667 1
< 0.1%
4144 1
< 0.1%
3646 1
< 0.1%
3597 1
< 0.1%
3516 1
< 0.1%
3400 1
< 0.1%
3286 1
< 0.1%
3189 1
< 0.1%
3054 1
< 0.1%

language
Categorical

IMBALANCE 

Distinct46
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
English
4676 
French
 
73
Spanish
 
40
Hindi
 
28
Mandarin
 
24
Other values (41)
 
157

Length

Max length10
Median length7
Mean length6.9811925
Min length4

Characters and Unicode

Total characters34892
Distinct characters43
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.4%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English 4676
93.6%
French 73
 
1.5%
Spanish 40
 
0.8%
Hindi 28
 
0.6%
Mandarin 24
 
0.5%
German 19
 
0.4%
Japanese 17
 
0.3%
Cantonese 11
 
0.2%
Italian 11
 
0.2%
Russian 11
 
0.2%
Other values (36) 88
 
1.8%

Length

2024-03-17T01:43:17.400703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 4676
93.6%
french 73
 
1.5%
spanish 40
 
0.8%
hindi 28
 
0.6%
mandarin 24
 
0.5%
german 19
 
0.4%
japanese 17
 
0.3%
cantonese 11
 
0.2%
italian 11
 
0.2%
russian 11
 
0.2%
Other values (36) 88
 
1.8%

Most occurring characters

ValueCountFrequency (%)
n 4997
14.3%
i 4876
14.0%
h 4817
13.8%
s 4799
13.8%
l 4703
13.5%
g 4694
13.5%
E 4676
13.4%
a 246
 
0.7%
e 213
 
0.6%
r 158
 
0.5%
Other values (33) 713
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29894
85.7%
Uppercase Letter 4998
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4997
16.7%
i 4876
16.3%
h 4817
16.1%
s 4799
16.1%
l 4703
15.7%
g 4694
15.7%
a 246
 
0.8%
e 213
 
0.7%
r 158
 
0.5%
c 88
 
0.3%
Other values (13) 303
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
E 4676
93.6%
F 74
 
1.5%
S 47
 
0.9%
H 34
 
0.7%
M 26
 
0.5%
G 20
 
0.4%
J 17
 
0.3%
P 17
 
0.3%
C 15
 
0.3%
I 15
 
0.3%
Other values (10) 57
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 34892
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4997
14.3%
i 4876
14.0%
h 4817
13.8%
s 4799
13.8%
l 4703
13.5%
g 4694
13.5%
E 4676
13.4%
a 246
 
0.7%
e 213
 
0.6%
r 158
 
0.5%
Other values (33) 713
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 4997
14.3%
i 4876
14.0%
h 4817
13.8%
s 4799
13.8%
l 4703
13.5%
g 4694
13.5%
E 4676
13.4%
a 246
 
0.7%
e 213
 
0.6%
r 158
 
0.5%
Other values (33) 713
 
2.0%
Distinct65
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:17.682083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length20
Median length3
Mean length3.4909964
Min length2

Characters and Unicode

Total characters17448
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.6%

Sample

1st rowUSA
2nd rowUSA
3rd rowUK
4th rowUSA
5th rowUSA
ValueCountFrequency (%)
usa 3778
74.6%
uk 443
 
8.7%
france 154
 
3.0%
canada 124
 
2.4%
germany 99
 
2.0%
australia 55
 
1.1%
india 34
 
0.7%
spain 33
 
0.7%
china 28
 
0.6%
italy 23
 
0.5%
Other values (63) 293
 
5.8%
2024-03-17T01:43:18.481357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 4223
24.2%
A 3848
22.1%
S 3845
22.0%
a 1082
 
6.2%
n 632
 
3.6%
K 476
 
2.7%
e 409
 
2.3%
r 403
 
2.3%
i 247
 
1.4%
d 216
 
1.2%
Other values (37) 2067
11.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13062
74.9%
Lowercase Letter 4320
 
24.8%
Space Separator 66
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1082
25.0%
n 632
14.6%
e 409
 
9.5%
r 403
 
9.3%
i 247
 
5.7%
d 216
 
5.0%
c 193
 
4.5%
l 154
 
3.6%
y 138
 
3.2%
m 125
 
2.9%
Other values (14) 721
16.7%
Uppercase Letter
ValueCountFrequency (%)
U 4223
32.3%
A 3848
29.5%
S 3845
29.4%
K 476
 
3.6%
C 159
 
1.2%
F 155
 
1.2%
G 102
 
0.8%
I 81
 
0.6%
N 30
 
0.2%
J 22
 
0.2%
Other values (12) 121
 
0.9%
Space Separator
ValueCountFrequency (%)
66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17382
99.6%
Common 66
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 4223
24.3%
A 3848
22.1%
S 3845
22.1%
a 1082
 
6.2%
n 632
 
3.6%
K 476
 
2.7%
e 409
 
2.4%
r 403
 
2.3%
i 247
 
1.4%
d 216
 
1.2%
Other values (36) 2001
11.5%
Common
ValueCountFrequency (%)
66
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 4223
24.2%
A 3848
22.1%
S 3845
22.0%
a 1082
 
6.2%
n 632
 
3.6%
K 476
 
2.7%
e 409
 
2.3%
r 403
 
2.3%
i 247
 
1.4%
d 216
 
1.2%
Other values (37) 2067
11.8%

content_rating
Categorical

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
R
2098 
PG-13
1444 
PG
698 
Not Rated
417 
G
 
112
Other values (13)
229 

Length

Max length9
Median length8
Mean length3.1846739
Min length1

Characters and Unicode

Total characters15917
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPG-13
2nd rowPG-13
3rd rowPG-13
4th rowPG-13
5th rowNot Rated

Common Values

ValueCountFrequency (%)
R 2098
42.0%
PG-13 1444
28.9%
PG 698
 
14.0%
Not Rated 417
 
8.3%
G 112
 
2.2%
Unrated 60
 
1.2%
Approved 55
 
1.1%
TV-14 30
 
0.6%
TV-MA 19
 
0.4%
TV-PG 13
 
0.3%
Other values (8) 52
 
1.0%

Length

2024-03-17T01:43:18.756882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r 2098
38.7%
pg-13 1444
26.7%
pg 698
 
12.9%
not 417
 
7.7%
rated 417
 
7.7%
g 112
 
2.1%
unrated 60
 
1.1%
approved 55
 
1.0%
tv-14 30
 
0.6%
tv-ma 19
 
0.4%
Other values (9) 65
 
1.2%

Most occurring characters

ValueCountFrequency (%)
R 2515
15.8%
G 2283
14.3%
P 2170
13.6%
- 1525
9.6%
1 1481
9.3%
3 1444
9.1%
t 894
 
5.6%
e 541
 
3.4%
d 541
 
3.4%
a 486
 
3.1%
Other values (18) 2037
12.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7720
48.5%
Lowercase Letter 3292
20.7%
Decimal Number 2963
 
18.6%
Dash Punctuation 1525
 
9.6%
Space Separator 417
 
2.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 2515
32.6%
G 2283
29.6%
P 2170
28.1%
N 424
 
5.5%
A 74
 
1.0%
T 74
 
1.0%
V 74
 
1.0%
U 60
 
0.8%
M 24
 
0.3%
X 13
 
0.2%
Other values (2) 9
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
t 894
27.2%
e 541
16.4%
d 541
16.4%
a 486
14.8%
o 472
14.3%
r 115
 
3.5%
p 110
 
3.3%
n 60
 
1.8%
v 55
 
1.7%
s 18
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 1481
50.0%
3 1444
48.7%
4 30
 
1.0%
7 8
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 1525
100.0%
Space Separator
ValueCountFrequency (%)
417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11012
69.2%
Common 4905
30.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 2515
22.8%
G 2283
20.7%
P 2170
19.7%
t 894
 
8.1%
e 541
 
4.9%
d 541
 
4.9%
a 486
 
4.4%
o 472
 
4.3%
N 424
 
3.9%
r 115
 
1.0%
Other values (12) 571
 
5.2%
Common
ValueCountFrequency (%)
- 1525
31.1%
1 1481
30.2%
3 1444
29.4%
417
 
8.5%
4 30
 
0.6%
7 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15917
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 2515
15.8%
G 2283
14.3%
P 2170
13.6%
- 1525
9.6%
1 1481
9.3%
3 1444
9.1%
t 894
 
5.6%
e 541
 
3.4%
d 541
 
3.4%
a 486
 
3.1%
Other values (18) 2037
12.8%

budget
Real number (ℝ)

SKEWED 

Distinct439
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37823658
Minimum218
Maximum1.22155 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:18.997132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile585500
Q17000000
median20000000
Q340000000
95-th percentile1.2045 × 108
Maximum1.22155 × 1010
Range1.22155 × 1010
Interquartile range (IQR)33000000

Descriptive statistics

Standard deviation1.9671219 × 108
Coefficient of variation (CV)5.2007712
Kurtosis2991.8614
Mean37823658
Median Absolute Deviation (MAD)15000000
Skewness50.469926
Sum1.8904264 × 1011
Variance3.8695686 × 1016
MonotonicityNot monotonic
2024-03-17T01:43:19.263458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000000 658
 
13.2%
15000000 141
 
2.8%
30000000 140
 
2.8%
25000000 140
 
2.8%
10000000 135
 
2.7%
40000000 130
 
2.6%
35000000 119
 
2.4%
5000000 110
 
2.2%
50000000 101
 
2.0%
60000000 92
 
1.8%
Other values (429) 3232
64.7%
ValueCountFrequency (%)
218 1
 
< 0.1%
1100 1
 
< 0.1%
1400 1
 
< 0.1%
3250 1
 
< 0.1%
4500 1
 
< 0.1%
7000 3
0.1%
9000 1
 
< 0.1%
10000 3
0.1%
13000 1
 
< 0.1%
14000 1
 
< 0.1%
ValueCountFrequency (%)
1.22155 × 10101
< 0.1%
4200000000 1
< 0.1%
2500000000 1
< 0.1%
2400000000 1
< 0.1%
2127519898 1
< 0.1%
1100000000 1
< 0.1%
1000000000 1
< 0.1%
700000000 2
< 0.1%
600000000 1
< 0.1%
553632000 1
< 0.1%

title_year
Real number (ℝ)

Distinct91
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.523
Minimum1916
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:19.516004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1916
5-th percentile1979
Q11999
median2005
Q32011
95-th percentile2015
Maximum2016
Range100
Interquartile range (IQR)12

Descriptive statistics

Standard deviation12.346385
Coefficient of variation (CV)0.006165415
Kurtosis7.7507055
Mean2002.523
Median Absolute Deviation (MAD)6
Skewness-2.3375399
Sum10008610
Variance152.43323
MonotonicityNot monotonic
2024-03-17T01:43:19.777903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 328
 
6.6%
2009 258
 
5.2%
2014 248
 
5.0%
2006 238
 
4.8%
2013 236
 
4.7%
2010 229
 
4.6%
2008 225
 
4.5%
2011 225
 
4.5%
2015 222
 
4.4%
2012 218
 
4.4%
Other values (81) 2571
51.4%
ValueCountFrequency (%)
1916 1
< 0.1%
1920 1
< 0.1%
1925 1
< 0.1%
1927 1
< 0.1%
1929 2
< 0.1%
1930 1
< 0.1%
1932 1
< 0.1%
1933 2
< 0.1%
1934 1
< 0.1%
1935 1
< 0.1%
ValueCountFrequency (%)
2016 103
 
2.1%
2015 222
4.4%
2014 248
5.0%
2013 236
4.7%
2012 218
4.4%
2011 225
4.5%
2010 229
4.6%
2009 258
5.2%
2008 225
4.5%
2007 202
4.0%

actor_2_facebook_likes
Real number (ℝ)

ZEROS 

Distinct917
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1640.2729
Minimum0
Maximum137000
Zeros55
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:20.019385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25.85
Q1281
median595
Q3912.75
95-th percentile11000
Maximum137000
Range137000
Interquartile range (IQR)631.75

Descriptive statistics

Standard deviation4026.0325
Coefficient of variation (CV)2.4544894
Kurtosis262.6445
Mean1640.2729
Median Absolute Deviation (MAD)317
Skewness10.024356
Sum8198084
Variance16208938
MonotonicityNot monotonic
2024-03-17T01:43:20.265512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 301
 
6.0%
11000 110
 
2.2%
2000 100
 
2.0%
3000 75
 
1.5%
0 55
 
1.1%
10000 46
 
0.9%
14000 40
 
0.8%
13000 40
 
0.8%
826 37
 
0.7%
4000 34
 
0.7%
Other values (907) 4160
83.2%
ValueCountFrequency (%)
0 55
1.1%
2 14
 
0.3%
3 14
 
0.3%
4 11
 
0.2%
5 10
 
0.2%
6 7
 
0.1%
7 4
 
0.1%
8 9
 
0.2%
9 13
 
0.3%
10 9
 
0.2%
ValueCountFrequency (%)
137000 1
 
< 0.1%
29000 1
 
< 0.1%
27000 2
 
< 0.1%
25000 3
 
0.1%
23000 6
0.1%
22000 11
0.2%
21000 3
 
0.1%
20000 6
0.1%
19000 7
0.1%
18000 9
0.2%

imdb_score
Real number (ℝ)

Distinct78
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4410564
Minimum1.6
Maximum9.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:20.511294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.4
Q15.8
median6.6
Q37.2
95-th percentile8.015
Maximum9.5
Range7.9
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.1241073
Coefficient of variation (CV)0.17452219
Kurtosis0.94155654
Mean6.4410564
Median Absolute Deviation (MAD)0.7
Skewness-0.74046501
Sum32192.4
Variance1.2636172
MonotonicityNot monotonic
2024-03-17T01:43:20.785104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7 221
 
4.4%
6.6 200
 
4.0%
7.2 193
 
3.9%
6.4 185
 
3.7%
6.5 184
 
3.7%
7.3 184
 
3.7%
7 181
 
3.6%
7.1 181
 
3.6%
6.8 180
 
3.6%
6.1 178
 
3.6%
Other values (68) 3111
62.2%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
1.7 1
 
< 0.1%
1.9 3
0.1%
2 2
< 0.1%
2.1 3
0.1%
2.2 3
0.1%
2.3 3
0.1%
2.4 2
< 0.1%
2.5 2
< 0.1%
2.6 2
< 0.1%
ValueCountFrequency (%)
9.5 1
 
< 0.1%
9.3 1
 
< 0.1%
9.2 1
 
< 0.1%
9.1 3
 
0.1%
9 3
 
0.1%
8.9 5
 
0.1%
8.8 7
 
0.1%
8.7 13
0.3%
8.6 15
0.3%
8.5 24
0.5%

aspect_ratio
Real number (ℝ)

Distinct22
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2298299
Minimum1.18
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:21.001706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile1.78
Q11.85
median2.35
Q32.35
95-th percentile2.35
Maximum16
Range14.82
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.3452716
Coefficient of variation (CV)0.60330681
Kurtosis95.99054
Mean2.2298299
Median Absolute Deviation (MAD)0
Skewness9.645703
Sum11144.69
Variance1.8097556
MonotonicityNot monotonic
2024-03-17T01:43:21.201236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2.35 2664
53.3%
1.85 1890
37.8%
1.78 108
 
2.2%
1.37 100
 
2.0%
1.33 67
 
1.3%
1.66 64
 
1.3%
16 45
 
0.9%
2.39 15
 
0.3%
2.2 14
 
0.3%
4 7
 
0.1%
Other values (12) 24
 
0.5%
ValueCountFrequency (%)
1.18 1
 
< 0.1%
1.2 1
 
< 0.1%
1.33 67
1.3%
1.37 100
2.0%
1.44 1
 
< 0.1%
1.5 2
 
< 0.1%
1.66 64
1.3%
1.75 3
 
0.1%
1.77 1
 
< 0.1%
1.78 108
2.2%
ValueCountFrequency (%)
16 45
 
0.9%
4 7
 
0.1%
2.76 3
 
0.1%
2.55 2
 
< 0.1%
2.4 3
 
0.1%
2.39 15
 
0.3%
2.35 2664
53.3%
2.24 1
 
< 0.1%
2.2 14
 
0.3%
2 5
 
0.1%

movie_facebook_likes
Real number (ℝ)

ZEROS 

Distinct876
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7487.4302
Minimum0
Maximum349000
Zeros2162
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-03-17T01:43:21.435437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median162.5
Q33000
95-th percentile40000
Maximum349000
Range349000
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation19290.727
Coefficient of variation (CV)2.5764149
Kurtosis41.774062
Mean7487.4302
Median Absolute Deviation (MAD)162.5
Skewness5.083321
Sum37422176
Variance3.7213213 × 108
MonotonicityNot monotonic
2024-03-17T01:43:21.683619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2162
43.3%
1000 109
 
2.2%
11000 82
 
1.6%
10000 81
 
1.6%
12000 61
 
1.2%
13000 58
 
1.2%
2000 56
 
1.1%
15000 52
 
1.0%
14000 49
 
1.0%
16000 47
 
0.9%
Other values (866) 2241
44.8%
ValueCountFrequency (%)
0 2162
43.3%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 5
 
0.1%
5 2
 
< 0.1%
7 3
 
0.1%
8 1
 
< 0.1%
9 3
 
0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
349000 1
< 0.1%
199000 1
< 0.1%
197000 1
< 0.1%
191000 1
< 0.1%
190000 1
< 0.1%
175000 1
< 0.1%
166000 1
< 0.1%
165000 1
< 0.1%
164000 1
< 0.1%
153000 1
< 0.1%

Interactions

2024-03-17T01:42:58.855403image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:19.174584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:22.109412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:25.206911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:28.090599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:30.850076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:33.551563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:36.484513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:39.268639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:42.019335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:44.877095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:47.585820image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:50.246085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:53.263227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:56.064729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:59.059689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:19.388594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:22.319114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:25.398202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:28.295834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:31.042389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:33.741948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:36.684182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:39.461630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:42.415371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:45.070124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:47.776174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:50.447298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:53.456595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:56.257845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:59.242373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:19.568265image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:22.498878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:25.570737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:28.488665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:31.217765image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:34.107371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:36.864716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:39.633431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:42.588389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:45.246551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:47.945372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:50.626235image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:53.633575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:56.433256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:59.418691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:19.749798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:22.683436image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:25.732886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:28.660618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:31.380771image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:34.280561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:37.038173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:39.802783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:42.751776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:45.419182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:48.109174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:50.801085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:53.804268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:56.606005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:59.590218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:19.930914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:22.864502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:25.906461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:28.824922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:31.551615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:34.445275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:37.208766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:39.973510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:42.913189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:45.586604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:48.274489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:50.987755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:53.972223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:56.776183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:59.984313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:20.140298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:23.078475image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:26.088629image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:29.019220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:31.726294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:34.628918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:37.399356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:40.156578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:43.098253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:45.762693image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:48.452226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:51.174798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:54.158327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:56.965712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:43:00.185743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:20.337455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:23.262948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:26.286833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:29.198700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:31.910280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:34.836490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:37.588824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:40.342552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:43.278840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:45.946352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:48.634162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:51.566983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:54.346958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:57.153111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:43:00.381055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:20.536069image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:23.459059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:26.491928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:29.379918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:32.096512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:35.026368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:37.778247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:40.535630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:43.465478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:46.135632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:48.814993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:51.764191image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:54.582584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:57.354073image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:43:00.562869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:20.728725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:23.641908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:26.675833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:29.560198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:32.275221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:35.205368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:37.961299image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:40.717746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:43.645515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:46.312921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:48.992860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:51.948478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:54.767350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:57.542613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:43:00.740814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:20.912485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:23.858548image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:26.844912image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:29.729673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:32.453137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:35.378859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:38.136941image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:40.898352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:43.811845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:46.486479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:49.161382image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:52.128386image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:54.947903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:57.744705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:43:00.925838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:21.102241image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:24.083531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:27.032218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:29.923801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:32.642062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:35.560378image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:38.325227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:41.092412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:43.991308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:46.660654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:49.345554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:52.313859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:55.133313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:57.925133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:43:01.099059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:21.287090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:24.282200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:27.307347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:30.108180image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:32.815518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:35.732554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:38.497448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:41.267173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:44.163342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:46.831861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:49.520851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:52.495675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:55.308445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:58.102264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:43:01.290798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:21.485321image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:24.488199image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:27.520501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:30.293348image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:33.002483image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:35.920809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:38.690576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:41.455083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:44.341716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:47.016889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:49.700191image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:52.688407image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:55.498483image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:58.287487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:43:01.483287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:21.684462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:24.676874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:27.700657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:30.479923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:33.183230image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:36.106799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:38.881909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:41.643094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:44.519892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:47.202923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:49.880044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:52.880387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:55.685219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:58.478489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:43:01.677491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:21.878911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:25.016060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:27.884752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:30.670574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:33.366574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:36.293265image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:39.075664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:41.832779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:44.697315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:47.396793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:50.059810image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:53.073675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:55.874825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-17T01:42:58.664601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-17T01:43:02.020355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-17T01:43:02.651616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

colordirector_namenum_critic_for_reviewsdurationdirector_facebook_likesactor_3_facebook_likesactor_2_nameactor_1_facebook_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_facebook_likesactor_3_nameplot_keywordsmovie_imdb_linknum_user_for_reviewslanguagecountrycontent_ratingbudgettitle_yearactor_2_facebook_likesimdb_scoreaspect_ratiomovie_facebook_likes
0ColorJames Cameron7231780855Joel David Moore1000760505847Action|Adventure|Fantasy|Sci-FiCCH PounderAvatar8862044834Wes Studiavatar|future|marine|native|paraplegichttp://www.imdb.com/title/tt0499549/?ref_=fn_tt_tt_13054EnglishUSAPG-1323700000020099367.91.7833000
1ColorGore Verbinski3021695631000Orlando Bloom40000309404152Action|Adventure|FantasyJohnny DeppPirates of the Caribbean: At World's End47122048350Jack Davenportgoddess|marriage ceremony|marriage proposal|pirate|singaporehttp://www.imdb.com/title/tt0449088/?ref_=fn_tt_tt_11238EnglishUSAPG-13300000000200750007.12.350
2ColorSam Mendes6021480161Rory Kinnear11000200074175Action|Adventure|ThrillerChristoph WaltzSpectre27586811700Stephanie Sigmanbomb|espionage|sequel|spy|terroristhttp://www.imdb.com/title/tt2379713/?ref_=fn_tt_tt_1994EnglishUKPG-1324500000020153936.82.3585000
3ColorChristopher Nolan8131642200023000Christian Bale27000448130642Action|ThrillerTom HardyThe Dark Knight Rises1144337106759Joseph Gordon-Levittdeception|imprisonment|lawlessness|police officer|terrorist plothttp://www.imdb.com/title/tt1345836/?ref_=fn_tt_tt_12701EnglishUSAPG-132500000002012230008.52.35164000
4ColorDoug Walker110103131369Rob Walker13125445749DocumentaryDoug WalkerStar Wars: Episode VII - The Force Awakens8143unknownnonehttp://www.imdb.com/title/tt5289954/?ref_=fn_tt_tt_1156EnglishUSANot Rated200000002005127.12.350
5ColorAndrew Stanton462132475530Samantha Morton64073058679Action|Adventure|Sci-FiDaryl SabaraJohn Carter2122041873Polly Walkeralien|american civil war|male nipple|mars|princesshttp://www.imdb.com/title/tt0401729/?ref_=fn_tt_tt_1738EnglishUSAPG-1326370000020126326.62.3524000
6ColorSam Raimi39215604000James Franco24000336530303Action|Adventure|RomanceJ.K. SimmonsSpider-Man 338305646055Kirsten Dunstsandman|spider man|symbiote|venom|villainhttp://www.imdb.com/title/tt0413300/?ref_=fn_tt_tt_11902EnglishUSAPG-132580000002007110006.22.350
7ColorNathan Greno32410015284Donna Murphy799200807262Adventure|Animation|Comedy|Family|Fantasy|Musical|RomanceBrad GarrettTangled2948102036M.C. Gainey17th century|based on fairy tale|disney|flower|towerhttp://www.imdb.com/title/tt0398286/?ref_=fn_tt_tt_1387EnglishUSAPG26000000020105537.81.8529000
8ColorJoss Whedon635141019000Robert Downey Jr.26000458991599Action|Adventure|Sci-FiChris HemsworthAvengers: Age of Ultron46266992000Scarlett Johanssonartificial intelligence|based on comic book|captain america|marvel cinematic universe|superherohttp://www.imdb.com/title/tt2395427/?ref_=fn_tt_tt_11117EnglishUSAPG-132500000002015210007.52.35118000
9ColorDavid Yates37515328210000Daniel Radcliffe25000301956980Adventure|Family|Fantasy|MysteryAlan RickmanHarry Potter and the Half-Blood Prince32179558753Rupert Grintblood|book|love|potion|professorhttp://www.imdb.com/title/tt0417741/?ref_=fn_tt_tt_1973EnglishUKPG2500000002009110007.52.3510000
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5033ColorShane Carruth143772918David Sullivan291424760Drama|Sci-Fi|ThrillerShane CarruthPrimer72639368Casey Goodenchanging the future|independent film|invention|nonlinear timeline|time travelhttp://www.imdb.com/title/tt0390384/?ref_=fn_tt_tt_1371EnglishUSAPG-1370002004457.01.8519000
5034ColorNeill Dela Llana358000Edgar Tancangco070071ThrillerIan GamazonCavite5890Quynn Tonjihad|mindanao|philippines|security guard|squatterhttp://www.imdb.com/title/tt0428303/?ref_=fn_tt_tt_135EnglishPhilippinesNot Rated7000200506.32.3574
5035ColorRobert Rodriguez568106Peter Marquardt1212040920Action|Crime|Drama|Romance|ThrillerCarlos GallardoEl Mariachi52055147Consuelo Gómezassassin|death|guitar|gun|mariachihttp://www.imdb.com/title/tt0104815/?ref_=fn_tt_tt_1130SpanishUSAR70001992206.91.370
5036ColorAnthony Vallone1108422John Considine4525445749Crime|DramaRichard JewellThe Mongol King3693Sara Stepnickajewell|mongol|nostradamus|stepnicka|vallonehttp://www.imdb.com/title/tt0430371/?ref_=fn_tt_tt_11EnglishUSAPG-1332502005447.82.354
5037ColorEdward Burns14950133Caitlin FitzGerald2964584Comedy|DramaKerry BishéNewlyweds1338690Daniella Pinedawritten and directed by cast memberhttp://www.imdb.com/title/tt1880418/?ref_=fn_tt_tt_114EnglishUSANot Rated900020112056.42.35413
5038ColorScott Smith1872318Daphne Zuniga63725445749Comedy|DramaEric MabiusSigned Sealed Delivered6292283Crystal Lowefraud|postal worker|prison|theft|trialhttp://www.imdb.com/title/tt3000844/?ref_=fn_tt_tt_16EnglishCanadaNot Rated2000000020134707.72.3584
5039Colorunknown434349319Valorie Curry84125445749Crime|Drama|Mystery|ThrillerNatalie ZeaThe Following738391753Sam Underwoodcult|fbi|hideout|prison escape|serial killerhttp://www.imdb.com/title/tt2071645/?ref_=fn_tt_tt_1359EnglishUSATV-142000000020055937.516.0032000
5040ColorBenjamin Roberds137600Maxwell Moody025445749Drama|Horror|ThrillerEva BoehnkeA Plague So Pleasant380David Chandlernonehttp://www.imdb.com/title/tt2107644/?ref_=fn_tt_tt_13EnglishUSANot Rated1400201306.32.3516
5041ColorDaniel Hsia141000489Daniel Henney94610443Comedy|Drama|RomanceAlan RuckShanghai Calling12552386Eliza Coupenonehttp://www.imdb.com/title/tt2070597/?ref_=fn_tt_tt_19EnglishUSAPG-132000000020127196.32.35660
5042ColorJon Gunn43901616Brian Herzlinger8685222DocumentaryJohn AugustMy Date with Drew4285163Jon Gunnactress name in title|crush|date|four word title|video camerahttp://www.imdb.com/title/tt0378407/?ref_=fn_tt_tt_184EnglishUSAPG11002004236.61.85456